title: A prediction model for cognitive performance in health ageing using diffusion tensor imaging with graph theory.
abstract: In this study, we employed diffusion tensor imaging (DTI) to construct brain structural network and then derive the connection matrices from 96 healthy elderly subjects. The correlation analysis between these topological properties of network based on graph theory and the Cognitive Abilities Screening Instrument (CASI) index were processed to extract the significant network characteristics. These characteristics were then integrated to estimate the models by various machine-learning algorithms to predict user's cognitive performance. From the results, linear regression model and Gaussian processes model showed presented better abilities with lower mean absolute errors of 5.8120 and 6.25 to predict the cognitive performance respectively. Moreover, these extracted topological properties of brain structural network derived from DTI also could be regarded as the bio-signatures for further evaluation of brain degeneration in healthy aged and early diagnosis of mild cognitive impairment (MCI).
authors.forename: Ruijuan
authors.initials: R
authors.lastname: Yun
authors.forename: Chung-Chih
authors.initials: CC
authors.lastname: Lin
authors.forename: Shuicai
authors.initials: S
authors.lastname: Wu
authors.forename: Chu-Chung
authors.initials: CC
authors.lastname: Huang
authors.forename: Ching-Po
authors.initials: CP
authors.lastname: Lin
authors.forename: Yi-Ping
authors.initials: YP
authors.lastname: Chao
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology* / Q000503:physiopathology; D060825:Cognitive Dysfunction / Q000175:diagnosis* / Q000503:physiopathology; D056324:Diffusion Tensor Imaging; D006801:Humans; D016014:Linear Models; D008297:Male; D016011:Normal Distribution